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During everyday work, individuals often engage in unplanned conversations that help them develop relationships, share information and coordinate tasks. Unfortunately, the work-from-home mandates issued due to the pandemic have reduced the frequency of unplanned conversations among employees. So, as businesses reopen, organizations are considering post-pandemic workplace solutions that can facilitate unplanned conversations. To aid these efforts and move research and theory on unplanned conversations forward, this study proposes and tests a multi-factor model of the antecedents of unplanned conversations.

This study adopted a multi-methodological approach and collected data using automated sensing technology, structured observations and cross-sectional survey methods. A total of 5,297 unplanned conversations among 61 employees were recorded using the custom mobile application and structured observations. Cross-sectional survey data about these employees' work contexts were also collected.

The study results showed that the model significantly predicted the frequency of unplanned conversations. Notably, technical expertise, perceived time pressure and team psychological safety significantly predicted the frequency of unplanned conversations. The study findings have both theoretical and practical significance.

Previous research studies have primarily focused on the influence of workplace designs on unplanned conversations. However, this study demonstrates that several other factors facilitate unplanned conversations. This research theorizes and empirically tests the relationship between unplanned conversations and several individual, team and organizational factors.

The purpose of this study is to examine the propagation of consumer and business confidence in the euro area with a particular focus on the global financial crisis (GFC), the European sovereign debt crisis (ESDC) and the COVID-19-induced Great Lockdown.

The authors apply Diebold and Yilmaz’s connectedness framework and the improved method based on the time-varying parameter vector autoregressive model.

The authors find that although the evolution of business confidence marked the GFC and the ESDC the role of consumer confidence (mainly in those countries with stricter containment and closure measures) increased in the COVID-19-induced crisis.

The findings are related to the different origins of the examined crisis periods, and the analysis of their interrelationship is a very relevant topic for future research.

This paper investigates the static and dynamic directional return spillovers and dependence among green investments, carbon markets, financial markets and commodity markets from January 2013 to September 2020.

This study employed both the quantile vector autoregression (QVAR) and time-varying parameter VAR (TVP-VAR) technique to examine the magnitude of static and dynamic directional spillovers and dependence of markets.

Results show that the magnitude of connectedness is extremely higher at quantile levels (q = 0.05 and q = 0.95) compared to those in the mean of the conditional distribution. This connotes that connectedness between green bonds and other assets increases with shock size for both negative and positive shocks. This further indicates that return shocks spread at a higher magnitude during extreme market conditions relative to normal periods. Additional analyses show the behavior of return transmission between green bond and other assets is asymmetric.

The findings of this study offer significant implications for portfolio investors, policymakers, regulatory authorities and investment community in terms of carefully assessing the unique characteristics offered by each markets in terms of return spillovers and dependence and diversifying the portfolios.

The study, first, uses a relatively new statistical technique, the QVAR advanced by Ando et al. (2018), to capture upper and lower tails’ quantile price connectedness and directional spillover. Therefore, the results possess adequate power against departure from mean-based conditional connectedness. Second, using a portfolio of green investments, carbon markets, financial markets and commodity markets, the uniqueness of this study lies in the examination of the static and dynamic dependence of the markets examined.

The purpose of this study was to investigate whether indicating victims of sexual attacks actively resisted their attacker or froze during their assault affected perceptions of victim blame, perpetrator blame and seriousness of the crime. We also tested whether victim and perpetrator gender or participants’ rape myth endorsement moderated the outcomes.

This study was a cross-sectional, vignette survey study with a 2 × 2 between-participants experimental design. Participants read a mock police report describing an alleged rape with a female or male victim who either resisted or froze, while perpetrator gender was adjusted heteronormatively.

Freezing and male victims were blamed more than resisting and female victims. Perpetrators were blamed more when the victim resisted, but male and female perpetrators were blamed equally. Seriousness of the crime was higher for male perpetrators and when the victim resisted. Female, but not male, rape myth acceptance moderated the relationship between victim behaviour and outcome variables.

This study highlights the influence of expectations about victim behaviour on perceptions of rape victims and the pervasive influence of rape myths when evaluating female rape victims. The data is drawn from the German border region of the Netherlands, which is an especially valuable population given the evolving legal definitions of rape in both countries.

To remain competitive, efficient and productive, organisations need to ensure that their employees continuously learn and develop. This is even more challenging and critical in times characterised by volatility, uncertainty, complexity and ambiguity (VUCA). Hence, several technological applications have been introduced with the promise to make organisational training and development (T&D) more efficient and targeted through digitisation and automation. However, digitising and automating processes in the sensitive field of T&D also poses challenges and perils for employees and organisations as a whole.

Structured by the T&D process of (1) assessment/planning, (2) design/implementation and (3) evaluation, the authors present different digitisation and automation possibilities and discuss the specific opportunities and challenges they pose. Subsequently, the authors identify and discuss overarching themes of opportunities and challenges of technology use in T&D via a meta-review.

This synthesis revealed three central topics that decision-makers in T&D should carefully consider when it comes to the implementation of technological applications: opportunities and challenges of (1) data collection, (2) decision-making and (3) the value of human contact.

This review integrates previously fragmented research on specific technologies applied to specific T&D functions and provides researchers and practitioners with a fuller picture of the opportunities and challenges of technology applied in T&D.

This study aims to scrutinize time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens and DeFi assets between 1 July 2018 and 19 February 2023 and determine optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.

This work examines time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens, and DeFi assets between 1 July 2018 and 19 February 2023. To this end, the time-varying parameter-vector autoregression (TVP-VAR)-based connectedness methodology of Antonakakis et al. (2020) This approach is an extended version of the Diebold–Yilmaz (DY) method (Diebold and Yılmaz, 2014) and has advantages over the original DY. First, unlike the DY, it is free of the selection of a particular window size. Second, it has robustness for the outliers. Furthermore, following Broadstock et al. (2022), the author estimates time-varying optimal portfolio weights and hedging effectiveness under different portfolio construction scenarios.

This study's results indicate the following results: (1) The overall connectedness indices prominently capture well-known financial/geopolitical distress incidents; (2) the leading cryptocurrencies (ETH, BTC and BNB) are the largest transmitter of return shocks, while LINK and BTC are the largest transmitters/recipients of volatility shocks; (3) cryptocurrencies, NFTs and DeFi form distinct cluster groups in terms of return and volatility connectedness; (4) the connectedness networks estimated around the 2022 cryptocurrency crash and the FTX's filing for the bankruptcy are characterized by the strongest return and volatility interlinkages; (5) optimal portfolio strategies computed by different portfolio construction techniques display similar motifs and have sustained growth paths except for some short-lived drop backs.

This study's findings imply several policy suggestions for investors, stakeholders and policymakers. First, the study's time-based dynamic interlinkages can help market participants in their optimal portfolio decisions. In particular, the persistent net receiving roles of the DeFi assets and the NFTs throughout the episode, especially around the financial/geopolitical turmoil, underpin their safe haven potentials (Umar et al., 2022a, b). Finally, since the total connectedness indices (TCIs) are prone to significantly increase around financial/geopolitical burst times, these tools can be valuable for policy makers to monitor risk.

The contribution of knowledge is at least threefold. First, the author focuses on the dynamic time interlinkages among major cryptocurrencies, NFTs and DeFi assets in July 2018 and February 2023 considering the prominent recent financial/geopolitical incidents. Second, the author estimates network topologies of dynamic connectedness around financial/geopolitical bursts and compared them in terms of interlinkages. Finally, the author calculates the time-varying optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.

This paper presents a qualitative study of penetration testing, the practice of attacking information systems to find security vulnerabilities and fixing them. The purpose of this paper is to understand whether and to what extent penetration testing can reveal various socio-organisational factors of information security in organisations. In doing so, the paper innovates theory by using Routine Activity Theory together with phenomenology of information systems concepts.

The articulation of Routine Activity Theory and phenomenology emerged inductively from the data analysis. The data consists of 24 qualitative interviews conducted with penetration testers, analysed with thematic analysis.

The starting assumption is that penetration testers are akin to offenders in a crime situation, dealing with targets and the absence of capable guardians. A key finding is that penetration testers described their targets as an installed base, highlighting how vulnerabilities, which make a target suitable, often emerge from properties of the existing built digital environments. This includes systems that are forgotten or lack ongoing maintenance. Moreover, penetration testers highlighted that although the testing is often predicated on planned methodologies, often they resort to serendipitous practices such as improvisation.

This paper contributes to theory, showing how Routine Activity Theory and phenomenological concepts can work together in the study of socio-organisational factors of information security. This contribution stems from considering that much research on information security focuses on the internal actions of organisations. The study of penetration testing as a proxy of real attacks allows novel insights into socio-organisational factors of information security in organisations.

The study examined the personal information management (PIM) challenges encountered by faculty in six universities in Ghana, their information refinding experiences and the perceived role of memory. The study tested the hypothesis that faculty PIM performance will significantly differ when the differences in the influence of personal factors (age, gender and rank) on their memory are considered.

The study was guided by a sample survey design. A questionnaire designed based on themes extracted from earlier interviews was used to collect quantitative data from 235 faculty members from six universities in Ghana. Data analysis was undertaken with a discrete multivariate Generalized Linear Model to investigate how memory intermediates in the relationship between age, gender and rank, and, refinding of stored information.

The paper identified two subfunctions of refinding (Refinding 1 and Refinding 2) associated with self-confidence in information re-finding, and, memory (Memory 1 and Memory 2), associated with the use of complimentary frames to locate previously found and stored information. There were no significant multivariate effects for gender as a stand-alone variable. Males who were aged less than 39 could refind stored information irrespective of the memory class. Older faculty aged 40–49 who possess Memory 1 and senior lecturers who possess Memory 2 performed well in refinding information. There was a statistically significant effect of age and memory; and rank and memory.

This study was limited to faculty in Ghana, whereas the study itself has implications for demographic differences in PIM.

Identifying how memory mediates the role of personal factors in faculty refinding of stored information will be necessary for the efforts to understand and design systems and technologies for enhancing faculty capacity to find/refind stored information.

Understanding how human memory can be augmented by technology is a great PIM strategy, but understanding how human memory and personal factors interplay to affect PIM is more important.

PIM of faculty has been extensively examined in the literature, and limitations of memory has always been identified as a constraint. Human memory has been augmented with technology, although the outcome has been very minimal. This study shows that in addition to technology augmentation, personal factors interplay with human memory to affect PIM. Discrete multivariate Generalized Linear Model applied in this study is an innovative way of addressing the challenges of assimilating statistical methodologies in psychosocial disciplines.

This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators.

In this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR.

This study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy.

According to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.

This study aims to explore how student recruitment practitioners at regional Australian universities strategise student recruitment efforts in Thailand. There is scarce research addressing regional universities, with prior studies focusing on metropolitan universities. Similarly, most prior studies have focused on high-volume markets, with little research exploring emerging markets such as Thailand.

Semi-structured interviews were conducted with student recruitment practitioners from regional universities that were responsible for recruiting Thai students. Thematic analysis was conducted to identify key themes.

Regional universities lack strategic ambidexterity in their approach to recruiting international students. They viewed Thailand as requiring longer term investment and were unwilling to dedicate their limited resources towards developing this market at the expense of other markets that would yield enrolments to contribute towards short-term targets.

Implications are provided with relevance to the student recruitment practitioner, with strategic ambidexterity discussed.

The paper fills a gap in the research by exploring international student recruitment and contextualising both regional universities and Thailand as a recruitment market. This study provides useful considerations that may be relevant to other emerging markets.